HU values of soft issues such as the brain stem, parotid and temporal lobes are very close, and largely different from those of bones in CT images. Previous studies [1]}, [2]}, [3]} used simple window width/level-based intensity transform for preprocessing, i.e., a lower threshold and a upper threshold with a linear function between them, which is shown in Fig. REF (a) and referred to as Naive Linear Functions (NLFs) in this paper. Processing CT images with an NLF is sub-optimal as it cannot obtain good visibility for soft tissues and bones at the same time, which may limit the segmentation accuracy. To address this issue and inspired by the fact that radiologists would use different window widths/levels to better differentiate OARs with diverse HU values, we propose to use Segmental Linear Functions (SLFs) to transform the CT images. More specifically, assume increasing numbers \([x_1, x_2, ..., x_K]\)  in [0,1] and corresponding \(K\)  HU values \([h_1, h_2, ..., h_K]\)  in \([h_{min}, h_{max}]\) , where \(h_{min}\)  and \(h_{max}\)  represent the minimal and maximal HU value of CT images, respectively. Assume the original HU value is \(h\) , and the transformed intensity \(x\)  is represented as:
\(x = \left\lbrace \begin{matrix}0, & h\le h_1\\h_i+\frac{h-h_i}{h_{i+1}-h_i}, & h_i < h \le h_{i+1}, \ i\in [1,K]\\1 , & h>h_K\end{matrix}\right.\) 
